Given the always increasing size of computer systems, manually protecting them in case of attacks is infeasible and error-prone. For this reason, several Intrusion Response Systems (IRSs) have been proposed so far, with the purpose of limiting the amount of work of an administrator. However, since the most advanced IRSs adopt a stateful approach, they are subject to what Bellman defined as the curse of dimensionality. In this paper, we propose an approach based on deep reinforcement learning which, to the best of our knowledge, has never been used until now for intrusion response. Experimental results show that the proposed approach reduces the time needed for the computation of defense policies by orders of magnitude, while providing near-optimal rewards.

Iannucci, S., Barba, O.D., Cardellini, V., Banicescu, I. (2019). A performance evaluation of deep reinforcement learning for model-based intrusion response. In Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019 (pp.158-163). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/FAS-W.2019.00047].

A performance evaluation of deep reinforcement learning for model-based intrusion response

Iannucci S.
;
2019-01-01

Abstract

Given the always increasing size of computer systems, manually protecting them in case of attacks is infeasible and error-prone. For this reason, several Intrusion Response Systems (IRSs) have been proposed so far, with the purpose of limiting the amount of work of an administrator. However, since the most advanced IRSs adopt a stateful approach, they are subject to what Bellman defined as the curse of dimensionality. In this paper, we propose an approach based on deep reinforcement learning which, to the best of our knowledge, has never been used until now for intrusion response. Experimental results show that the proposed approach reduces the time needed for the computation of defense policies by orders of magnitude, while providing near-optimal rewards.
2019
978-1-7281-2406-3
Iannucci, S., Barba, O.D., Cardellini, V., Banicescu, I. (2019). A performance evaluation of deep reinforcement learning for model-based intrusion response. In Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019 (pp.158-163). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/FAS-W.2019.00047].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/404584
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 9
social impact